Version 2.0
Estimations relating to property counts, and consumption by unmetered customers, are used in the calculation of Leakage. The aim of this hypothesis is to identify potential inaccuracies in these estimations, which may affect the calculation of Leakage.
An average daily consumption for metered household and non-household customers has been derived. Consumption was then extrapolated to include non-metered customers, deriving a basic estimate of consumption for each DMA. This was then compared with Nightflow totals; it is expected that these would generally correlate, with higher Nightflow being recorded in DMAs with higher consumption. However, some DMAs were identified in which the Nightflow was significantly higher than would be expected based on estimated consumption, and these have therefore been highlighted for recommended further investigation; some of these appear to be related to discrepancies in different sources of property data.
In addition, Household Meter Readings have been analysed in order to determine if consumption patterns are different for customers using AMR meters, in comparison to other meter types. It was observed that AMR Meters generally record slightly higher consumption than other meter types, with older AMR meters recording less consumption than those less than five years old.
Currently, 46% of Non-Household (NHH) Customers are unmetered, and their water consumption is therefore estimated. This estimated affects the Leakage calculation. The purpose of this investigation is to use available data to derive a nominal consumption, and compare this to estimated Leakage data, in the context of property data discrepancies.
The discrepancy between R&P properties and actual properties (derived from ABP and previously Addresspoint datasets) could have an impact in the leakage calculation; more specifically the night-use allowance.
R&P property counts are all the properties in the Southern Water billing system which is not geocoded. Addresspoint is a geocoded data set which has now been superseded by the Address Base Premium (ABP) dataset.
OFWAT Leakage Calculation
Approximately 10% of AMR Meters do not report readings. It is hypothesised that these may behave differently from other types of meter, and the estimated consumption of this 10% is leading to errors in the calculation of Leakage.
The following datasets are used in the investigation of this hypothesis:
This data was supplied by Southern Water, and covers the period from 1 April 2016 to 31 March 2019. The data is based on meter readings, with consumption derived based on the differences between readings.
At the start and ends of this three year period, the sample size becomes smaller, and therefore, investigations were focussed on the middle year of this data, 1 April 2017 to 31 March 2018, for which an average consumption per property, per day, has been derived. This is done based on a simple average of water used per day, spread evenly between meter readings. It does not account for seasonal variations in consumption at individiual property level, although given the large size of the dataset, and the fact that household meter readings are taken almost daily, this method is expected to give a general picture of seasonal consumption.
This data is divided into household and non-household customers. This data is then extrapolated to include unmetered customers, by taking the average customer consumption, per day, in each DMA, and applying this to each unmetered customer.
The results of this are displayed below. It should be noted that the scale of each graph is adjusted for clarity.
This data can be summarised more generally as follows.
It should be noted that this is a simple estimation of seasonal consumption, for the purposes of comparing this with nightflow data, and is not intended to replicate or replace the leakage calculation.
This data is used to evaluate consumption by customers in comparison to customers with other types of meter. Consumption is compared accross different regions, as well as by the age of AMR meters, with meters over five years old classed as ‘Old’ and those more recently installed classed as ‘New’. This analysis includes only those where there exist multiple meter readings covering a time period of over 14 consecutive days.
The distribution of meter types is as follows:
| New AMR Meters | Old AMR Meters | Other Meter Types |
|---|---|---|
| 123061 | 418010 | 371956 |
There is a 9% discrepancy between the R&P property count and ABP count. This has jumped from the 1% discrepancy from Addresspoint dataset. In order to reconcile the property counts, a DMA factor is applied to scale down the property count on a WBA level. There is a 8% difference between ABP and Addresspoint Factor applied.
| WBA | R&P Properties | ABP Properties | ABP DMA Factor | Addresspoint Properties | Addresspoint DMA Factor |
|---|---|---|---|---|---|
| Andover | 32,778 | 35,675 | 0.92 | 32,193 | 1.02 |
| Hampshire South | 279,618 | 307,608 | 0.91 | 288,641 | 0.97 |
| Hastings | 54,068 | 58,946 | 0.92 | 50,386 | 1.07 |
| Isle of Wight | 72,124 | 83,195 | 0.87 | 74,332 | 0.97 |
| Kingsclere | 6,681 | 7,053 | 0.95 | 6,847 | 0.98 |
| Medway | 205,535 | 220,762 | 0.93 | 208,060 | 0.99 |
| Sussex Coastal | 253,587 | 285,080 | 0.89 | 249,925 | 1.01 |
| Sussex North | 116,044 | 125,763 | 0.92 | 117,898 | 0.98 |
| Thanet | 95,543 | 102,432 | 0.93 | 96,041 | 0.99 |
| TOTAL | 1,115,978 | 1,226,514 | 0.91 | 1,124,323 | 0.99 |
The estimated consumption, as derived in Section 3 above, was then plotted against the total Nightflow for the same DMA in the same period (2017-18). It would generally be expected that this comparison would show a high degree of correlation.
DMAs are coloured on this plot based on the discrepancy between R&P and ABP Property Data.
It can be seen from the above data that there are several significant outliers, in which the recorded nightflow is significantly higher than what would be expected, given the consumption estimates.
Therefore, the top 20 DMAs, ordered by total Nightflow, are shown below.
| DMA | Total Consumption (2017-18) | Total Nightflow (2017-18) | Property Discrepancy |
|---|---|---|---|
| SL30 | 310550.81 | 728460.1 | 1189 |
| CR10 | 313703.96 | 676442.0 | 729 |
| MS18 | 235282.90 | 607281.0 | 2918 |
| OS11 | 117407.79 | 605713.0 | 3316 |
| LS01 | 306169.10 | 498155.0 | 654 |
| WG31 | 72328.57 | 472354.0 | 116 |
| WM06 | 194102.43 | 459265.0 | 397 |
| CW70 | 255752.86 | 448604.5 | 63 |
| SL50 | 229640.17 | 448585.0 | 725 |
| FL33 | 440197.02 | 423839.5 | 971 |
| TW27 | 325288.69 | 422604.0 | 1036 |
| MS23 | 353939.20 | 403868.0 | 1445 |
| CW72 | 250145.90 | 380838.0 | 726 |
| CR04 | 283824.72 | 377704.0 | 456 |
| CR30 | 294027.99 | 348382.1 | 676 |
| WL04 | 129368.09 | 347655.0 | 1186 |
| DL10 | 174300.02 | 342350.0 | 472 |
| SL31 | 237260.27 | 318062.5 | 98 |
| BW16 | 248977.49 | 312230.0 | 781 |
| AN10 | 242658.15 | 305574.0 | 373 |
Property Discrepancies (as per the scatter plot above) are identified on the map below. The top 20 DMAs, in terms of nightflow (as per the above table), are highlighted with a blue border. A number of these are known to be in an area of significant property development in North Kent.